The document discusses the concept of 'configuration smells' in infrastructure as code, focusing on the identification of design and implementation issues that impact maintainability. It presents a taxonomy for these smells based on an analysis of over 4,600 Puppet repositories and explores correlations between different types of smells. The study provides insights into best practices for writing maintainable configuration code and highlights tools for detecting such smells.

1. Does Your Configuration
Code Smell?
Tushar Sharma, Marios Fragkoulis,
Diomidis Spinellis
{tushar, mfg, dds}@aueb.gr
MSR 2016

2. “Smells”
Code Smell
…certain structures in the code
that suggest (sometimes they
scream for) the possibility of
refactoring.

4. “Smells”
Design Smells
“Design smells are certain
structures in the design that
indicate violation of
fundamental design principles
and negatively impact design
quality.”

5. “Smells”
Configuration Smells

6. “Infrastructure as Code” (IaC)

7. Software System
IaC and Traditional SE
Production code
Infrastructure,
configuration
code, tools and
services
Apply
traditional
software
engineering
practices

8. Puppet example
package { 'apache2':
require => Exec['apt-update'],
ensure => installed,
}
service { 'apache2':
ensure => running,
}
user { 'tushar':
ensure => present,
uid => '1000',
gid => '1000',
shell => '/bin/bash',
home => '/home/tushar'
}
Configuration management tools:
Ansible, Chef, CFEngine, Puppet

9. Overview of the study
Taxonomy of
configuration smells
Research
questions
Configuration code
(Puppet - 4621
repositories)
Results
Tools to detect
configuration smells

10. Configuration smells: A taxonomy

11. Implementation configuration smells

12. Design configuration smells
• Multifaceted Abstraction
• elements of an abstraction are not cohesive.
• Unnecessary Abstraction
• an empty class, ‘define’, or module
• Imperative Abstraction
• imperative statements
• Missing Abstraction
• resources and language elements are not encapsulated in an
abstraction.

13. Design configuration smells
• Duplicate Block
• a duplicate block of statements
• Deficient Encapsulation
• global variables in a node declaration or ENC
• Insufficient Modularization
• large and complex abstraction
• Unstructured Module
• obscure module and repository structure

14. Design configuration smells
• Dense Structure
• repository has excessive and dense dependencies
• Weakened Modularity
• a module with high coupling and low cohesion
• Broken Hierarchy
• inter-namespace inheritance

15. Mining GitHub Repositories
Repositories 4,621
Puppet files 142,662
Class declarations 132,323
Define declarations 39,263
File resources 117,286
Package resources 49,841
Service resources 18,737
Exec declarations 43,468
Lines of code (Puppet only) 8,948,611
GHTorrent

16. Detecting configuration smells
Implementation configuration smells
• Additional custom rules to an existing tool
Design configuration smells
• Puppeteer [1]
1. https://github.com/tushartushar/Puppeteer

17. 1. What is the distribution of maintainability
smells in configuration code?
ICS #I(V) #I(E)
IMD 4,604 706
INC 4,804 440
ICE 3,994 964
IDE 65 29
IMA 22,976 1,383
IIA 7,80,265 3,064
IPV 14,360 729
IIT 11,071 1,467
IDS 6,466 674
IQU 4,28,951 2,463
ILS 5,27,637 4,115
IIC 4,797 1,217
IUV 71,339 1,405
DCS #I(V) #I(E)
DMF 64,266 4,339
DUA 4,319 1,427
DIA 4,354 1,575
DMA 1,913 813
DIM 96,033 4,422
DUM 4,653 3,337
DDB 17,601 1,016
DBH 83 37
DDS 1,760 1,760
DDE 1,075 424
DWM 13,944 2,890

18. 1. What is the distribution of maintainability
smells in configuration code?
ICS #I(V) #I(E)
IMD 4,604 706
INC 4,804 440
ICE 3,994 964
IDE 65 29
IMA 22,976 1,383
IIA 7,80,265 3,064
IPV 14,360 729
IIT 11,071 1,467
IDS 6,466 674
IQU 4,28,951 2,463
ILS 5,27,637 4,115
IIC 4,797 1,217
IUV 71,339 1,405
DCS #I(V) #I(E)
DMF 64,266 4,339
DUA 4,319 1,427
DIA 4,354 1,575
DMA 1,913 813
DIM 96,033 4,422
DUM 4,653 3,337
DDB 17,601 1,016
DBH 83 37
DDS 1,760 1,760
DDE 1,075 424
DWM 13,944 2,890
Tools and IDEs
are still maturing.
Either
developers do not
duplicate code at all
or do it massively

19. 2. What is the relationship between the
occurrence of design configuration smells and
implementation configuration smells?
CORRELATION P-VALUE
Analysis by volume 0.6641 <2.2e-16
Analysis by existance 0.4452 <2.2e-16

20. 3. Is the principle of coexistence applicable to
smells in configuration projects?
Whenever duplicate entity or
broken hierarchy smells are found, it is very
likely to find other smells from the same
category in the project.
Design configuration
smells show 9.3% higher co-occurrence
among themselves than the implementation
configuration smells.

21. 4. Does smell density depend on the size of the
configuration project?
CORRELATION P-VALUE
Implementation smells 0.0383 0.00980
Design smells -0.3285 <2.2e-16

22. Contributions
Research
• Taxonomy for configuration smells
• A method for investigating system configuration smells
by mining repositories
Practice
• Best practices to write maintainable configuration code
• Tools to identify configuration smells

23. Thank you!!
Courtesy: spikedmath.com
Tushar Sharma
tushar@aueb.gr
@Sharma__Tushar